ArticlePloS one2019
Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04682756 (A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model), which is not on this map. Cited by 45 papers, 4 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model
Who cites it
45 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- In-hospital mortality risk prediction models for patients with acute coronary syndrome: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Prediction of Hospital Mortality in Patients with ST Segment Elevation Myocardial Infarction: Evolution of Risk Measurement Techniques and Assessment of Their Effectiveness (Review).Sovremennye tekhnologii v meditsine · 2024Pooled it
- The predictive value of machine learning for mortality risk in patients with acute coronary syndromes: a systematic review and meta-analysis.European journal of medical research · 2023Pooled it
- Machine Learning-Based Risk Prediction of In-Hospital Mortality in Patients With Non-ST-Elevation Acute Coronary Syndrome at Various Stages of the Diagnostic Process: Observational Study.JMIR medical informatics · 2026Observational
- Artificial Intelligence in Cardiovascular Risk Prediction: An Up-to-Date Narrative Review on the Emerging Role of Lipid Profile-Based Models.Journal of clinical medicine · 2026Review
- Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.BMC medical informatics and decision making · 2026Article
- Development and validation of a neural network survival prediction model for ischemic heart disease.Cardiovascular diabetology · 2026Article
- Artificial Intelligence Tools in Myocardial Infarction Prognosis: Evaluating the Performance of Machine Learning and Deep Learning Models.Current cardiology reviews · 2026Review
- Comparing Machine Learning Models for Predicting Mortality after Myocardial Infarction: A Systematic Review and Meta-analysis.Archives of academic emergency medicine · 2026Review
- Applied machine learning to predict 1-year major adverse cardiovascular events in elderly patients after percutaneous coronary intervention.BMC medical informatics and decision making · 2025Article
- Data Augmentation-Enhanced Myocardial Infarction Classification and Localization Using a ResNet-Transformer Cascaded Network.Biology · 2025Article
- Machine-learning based calculator for personalized risk assessment following ST-elevation myocardial infarction.BMC cardiovascular disorders · 2025Article
- A Multitask Deep Learning Model for Predicting Myocardial Infarction Complications.Bioengineering (Basel, Switzerland) · 2025Article
- Machine learning integration of echocardiographic and clinical data to improve prediction of survival following myocardial infarction.European heart journal open · 2025Article
- Survival prediction modelling in patients with acute ST-segment elevation myocardial infarction with LASSO regression and explainable machine learning.Frontiers in medicine · 2025Article
- Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model.Frontiers in cardiovascular medicine · 2025Article
- Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges.The Canadian journal of cardiology · 2024Review
- Review
- Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system.Journal of the American Medical Informatics Association : JAMIA · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveConventional risk stratification models for mortality of acute myocardial infarction (AMI) have potential limitations. This study aimed to develop and validate deep-learning-based risk stratification for the mortality of patients with AMI (DAMI).
methodsThe data of 22,875 AMI patients from the Korean working group of the myocardial infarction (KorMI) registry were exclusively divided into 12,152 derivation data of 36 hospitals and 10,723 validation data of 23 hospitals. The predictor variables were the initial demographic and laboratory data. The endpoints were in-hospital mortality and 12-months mortality. We compared the DAMI performance with the global registry of acute coronary event (GRACE) score, acute coronary treatment and intervention outcomes network (ACTION) score, and the thrombolysis in myocardial infarction (TIMI) score using the validation data.
resultsIn-hospital mortality for the study subjects was 4.4% and 6-month mortality after survival upon discharge was 2.2%. The areas under the receiver operating characteristic curves (AUCs) of the DAMI were 0.905 [95% confidence interval 0.902-0.909] and 0.870 [0.865-0.876] for the ST elevation myocardial infarction (STEMI) and non ST elevation myocardial infarction (NSTEMI) patients, respectively; these results significantly outperformed those of the GRACE (0.851 [0.846-0.856], 0.810 [0.803-0.819]), ACTION (0.852 [0.847-0.857], 0.806 [0.799-0.814] and TIMI score (0.781 [0.775-0.787], 0.593[0.585-0.603]). DAMI predicted 30.9% of patients more accurately than the GRACE score. As secondary outcome, during the 6-month follow-up, the high risk group, defined by the DAMI, has a significantly higher mortality rate than the low risk group (17.1% vs. 0.5%, p < 0.001).
conclusionsThe DAMI predicted in-hospital mortality and 12-month mortality of AMI patients more accurately than the existing risk scores and other machine-learning methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.